Graph Transformer
graph-transformer-6eb10b9a·2 events·first seen 28d agoAliases: Graph Transformer
Co-occurring entities
More like this (12)
Recent events (2)
Graph Classification with Transformers
A Hugging Face blog post covering the application of transformer architectures to graph classification tasks. The post likely discusses how attention mechanisms can be adapted for graph-structured data, bridging the gap between standard transformer models and graph machine learning. This represents a methodological intersection of two active research areas in ML.
Trajectory Analysis of Masked Diffusion LMs for Graph-to-Text Generation with Lambda-Scaled Structural Decoding
This paper presents the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation, analyzing the order in which tokens are unmasked during iterative decoding. The authors find MDLMs naturally unmask entities first, then relational/function words, then structural tokens—a pattern disrupted by supervised fine-tuning, which prematurely anchors structural tokens and causes hallucination or omission. They propose lambda-scaled structural decoding, a training-free inference-time fix that recovers +9.4 BLEU-4, and introduce Graph-LLaDA, which integrates a Graph Transformer encoder into LLaDA's decoding process. Cross-dataset evaluation on the LAGRANGE benchmark shows prior baselines overfit to dataset-specific patterns while MDLM-based approaches generalize better.